In this paper, we present a novel compression technique for Bidirectional Texture Functions based on a sparse tensor decomposition. We apply the K-SVD algorithm along two different modes of a tensor to decompose it into a small dictionary and two sparse tensors. This representation is very compact, allowing for considerably better compression ratios at the same RMS error than possible with current compression techniques like PCA, N-mode SVD and Per Cluster Factorization. In contrast to other tensor decomposition based techniques, the use of a sparse representation achieves a rendering performance that is at high compression ratios similar to PCA based methods.

Museums and Cultural Heritage institutions have a growing interest in presenting their collections to a broader community via the Internet. The photo-realistic presentation of interactively inspectable virtual surrogates is one of the most challenging problems in this field. For this purpose, we seek to employ not only a 3D geometry but also a powerful material representation capable of reproducing the full visual appeal of an object. In this paper, we propose a WebGL-based presentation framework in which reflectance information is represented via Bidirectional Texture Functions. Our approach works out-of-the-box in modern web browsers and allows for the progressive transmission and interactive rendering of digitized artifacts consisting of 3D geometry and reflectance information. We handle the huge amount of data needed for this representation by employing a novel progressive streaming approach for BTFs which allows for the smooth interactive inspection of a steadily improving version during the download. We demonstrate an interesting use-case of this technique at a cross-section of Cultural Heritage, medical education and research and provide an evaluation of the capabilities of our framework in the scope of BTF compression and transmission.

Abstract-In this work, we present a framework for multicamera, multi-projector object acquisition based on structured light. This approach allows the reconstruction of an object without moving either the object or the acquisition setup, avoiding any registration of independent measurements. To overcome the resolution limitations of the individual projectors, we introduce a novel super-resolution scheme. By exploiting high dynamic range imaging, we are able to handle even complicated objects, exhibiting strong specularities. We show that, combined with an iterated bundle adjustment, these improvements increase the accuracy of the obtained point cloud.

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In this paper, we present a novel exemplar-based technique for the interpolation between two textures that combines patch-based and statistical approaches. Motivated by the notion of texture as a largely local phenomenon, we warp and blend small image neighborhoods prior to patch-based texture synthesis. In addition, interpolating and enforcing characteristic image statistics faithfully handles high frequency detail. We are able to create both intermediate textures as well as continuous transitions. In contrast to previous techniques computing a global morphing transformation on the entire input exemplar images, our localized and patch-based approach allows us to successfully interpolate between textures with considerable differences in feature topology for which no smooth global warping field exists.